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# What does this PR do? the build.yaml is only used in the following ways: 1. list-deps 2. distribution code-gen since `llama stack build` no longer exists, I found myself asking "why do we need two different files for list-deps and run"? Removing the BuildConfig and altering the usage of the DistributionTemplate in llama stack list-deps is the first step in removing the build yaml entirely. Removing the BuildConfig and build.yaml cuts the files users need to maintain in half, and allows us to focus on the stability of _just_ the run.yaml This PR removes the build.yaml, BuildConfig datatype, and its usage throughout the codebase. Users are now expected to point to run.yaml files when running list-deps, and our codebase automatically uses these types now for things like `get_provider_registry`. **Additionally, two renames: `StackRunConfig` -> `StackConfig` and `run.yaml` -> `config.yaml`.** The build.yaml made sense for when we were managing the build process for the user and actually _producing_ a run.yaml _from_ the build.yaml, but now that we are simply just getting the provider registry and listing the deps, switching to config.yaml simplifies the scope here greatly. ## Test Plan existing list-deps usage should work in the tests. --------- Signed-off-by: Charlie Doern <cdoern@redhat.com> |
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Llama Stack
Quick Start | Documentation | Colab Notebook | Discord
🚀 One-Line Installer 🚀
To try Llama Stack locally, run:
curl -LsSf https://github.com/llamastack/llama-stack/raw/main/scripts/install.sh | bash
Overview
Llama Stack defines and standardizes the core building blocks that simplify AI application development. It provides a unified set of APIs with implementations from leading service providers. More specifically, it provides:
- Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals.
- Plugin architecture to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile.
- Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly and reliably in any environment.
- Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android.
- Standalone applications as examples for how to build production-grade AI applications with Llama Stack.
Llama Stack Benefits
- Flexibility: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices.
- Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior.
- Robust Ecosystem: Llama Stack is integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models.
For more information, see the Benefits of Llama Stack documentation.
API Providers
Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack. Please checkout for full list
| API Provider | Environments | Agents | Inference | VectorIO | Safety | Post Training | Eval | DatasetIO |
|---|---|---|---|---|---|---|---|---|
| Meta Reference | Single Node | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| SambaNova | Hosted | ✅ | ✅ | |||||
| Cerebras | Hosted | ✅ | ||||||
| Fireworks | Hosted | ✅ | ✅ | ✅ | ||||
| AWS Bedrock | Hosted | ✅ | ✅ | |||||
| Together | Hosted | ✅ | ✅ | ✅ | ||||
| Groq | Hosted | ✅ | ||||||
| Ollama | Single Node | ✅ | ||||||
| TGI | Hosted/Single Node | ✅ | ||||||
| NVIDIA NIM | Hosted/Single Node | ✅ | ✅ | |||||
| ChromaDB | Hosted/Single Node | ✅ | ||||||
| Milvus | Hosted/Single Node | ✅ | ||||||
| Qdrant | Hosted/Single Node | ✅ | ||||||
| Weaviate | Hosted/Single Node | ✅ | ||||||
| SQLite-vec | Single Node | ✅ | ||||||
| PG Vector | Single Node | ✅ | ||||||
| PyTorch ExecuTorch | On-device iOS | ✅ | ✅ | |||||
| vLLM | Single Node | ✅ | ||||||
| OpenAI | Hosted | ✅ | ||||||
| Anthropic | Hosted | ✅ | ||||||
| Gemini | Hosted | ✅ | ||||||
| WatsonX | Hosted | ✅ | ||||||
| HuggingFace | Single Node | ✅ | ✅ | |||||
| TorchTune | Single Node | ✅ | ||||||
| NVIDIA NEMO | Hosted | ✅ | ✅ | ✅ | ✅ | ✅ | ||
| NVIDIA | Hosted | ✅ | ✅ | ✅ |
Note
: Additional providers are available through external packages. See External Providers documentation.
Distributions
A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider implementations for each API component. Distributions make it easy to get started with a specific deployment scenario. For example, you can begin with a local setup of Ollama and seamlessly transition to production, with fireworks, without changing your application code. Here are some of the distributions we support:
| Distribution | Llama Stack Docker | Start This Distribution |
|---|---|---|
| Starter Distribution | llamastack/distribution-starter | Guide |
| Meta Reference | llamastack/distribution-meta-reference-gpu | Guide |
| PostgreSQL | llamastack/distribution-postgres-demo |
For full documentation on the Llama Stack distributions see the Distributions Overview page.
Documentation
Please checkout our Documentation page for more details.
- CLI references
- llama (server-side) CLI Reference: Guide for using the
llamaCLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. - llama (client-side) CLI Reference: Guide for using the
llama-stack-clientCLI, which allows you to query information about the distribution.
- llama (server-side) CLI Reference: Guide for using the
- Getting Started
- Quick guide to start a Llama Stack server.
- Jupyter notebook to walk-through how to use simple text and vision inference llama_stack_client APIs
- The complete Llama Stack lesson Colab notebook of the new Llama 3.2 course on Deeplearning.ai.
- A Zero-to-Hero Guide that guide you through all the key components of llama stack with code samples.
- Contributing
- Adding a new API Provider to walk-through how to add a new API provider.
Llama Stack Client SDKs
Check out our client SDKs for connecting to a Llama Stack server in your preferred language.
| Language | Client SDK | Package |
|---|---|---|
| Python | llama-stack-client-python | |
| Swift | llama-stack-client-swift | |
| Typescript | llama-stack-client-typescript | |
| Kotlin | llama-stack-client-kotlin |
You can find more example scripts with client SDKs to talk with the Llama Stack server in our llama-stack-apps repo.
🌟 GitHub Star History
Star History
✨ Contributors
Thanks to all of our amazing contributors!